标题:Feature-Based Online Representation Algorithm for Streaming Time Series Similarity Search
作者:Zhan P.; Sun C.; Hu Y.; Luo W.; Zheng J.; Li X.
作者机构:[Zhan, P] School of Software, Shandong University, Jinan, Shandong, China;[ Sun, C] School of Computer Science and Technology, Shandong University, Qi 更多
通讯作者:Zheng, J(zhengjiecai@sdu.edu.cn)
通讯作者地址:[Zheng, J] School of Sport Communication and Information Technology, Shandong Sport UniversityChina;
来源:International Journal of Pattern Recognition and Artificial Intelligence
出版年:2019
DOI:10.1142/S021800142050010X
关键词:feature representation; Pattern recognition; similarity search; streaming time series
摘要:With the rapid development of information technology, we have already access to the era of big data. Time series is a sequence of data points associated with numerical values and successive timestamps. Time series not only has the traditional big data features, but also can be continuously generated in a high speed. Therefore, it is very time- and resource-consuming to directly apply the traditional time series similarity search methods on the raw time series data. In this paper, we propose a novel online segmenting algorithm for streaming time series, which has a relatively high performance on feature representation and similarity search. Extensive experimental results on different typical time series datasets have demonstrated the superiority of our method. © 2020 World Scientific Publishing Company.
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072214290&doi=10.1142%2fS021800142050010X&partnerID=40&md5=1472244d7e2024475e9343ed45ccd000
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